This chapter introduces software platforms to be used throughout the book. We will cover this topic from two perspectives: First, we will consider PythonPython running on PC. Python will allow the reader to implement and test the desired machine learning algorithm offline. Besides, it will help the reader to visualize data obtained from sensors. Hence, it can be analyzed easily. Our second perspective will be from the microcontrollerMicrocontroller side. The microcontroller itself is of no use without being programmed. Therefore, we need an integrated development environment (IDE) working on PC. The IDE allows forming a project including all necessary support files and code for the microcontroller. We can build the project on PC and debug it on the microcontroller if the IDE has such an option. Then, we can embed and run the generated executable file on the microcontroller. We will consider STM32CubeIDE, Mbed Studio, and Keil Studio CloudKeil Studio Cloud platforms for this purpose. To be more specific, we will benefit from STM32CubeIDESTM32CubeIDE for C and C++ language-based programming. We will use Mbed Studio and Keil Studio CloudKeil Studio Cloud platforms for C++ language-based programming. As the end-of-chapter application, we will provide tools for analyzing the generated code. Hence, the reader can measure the memory usage and inference time of a given machine learning code. This will be of great help in the following chapters.